How do I install the Performing Memory Forensics With Volatility3 skill?

Run npx skillmds@latest add xalgord/performing-memory-forensics-with-volatility3 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Performing Memory Forensics With Volatility3 skill do?

Analyze volatile memory dumps using Volatility 3 to extract running processes, network connections, loaded modules, and evidence of malicious activity. It is listed under Coding & Dev Tools on SkillMD.

Is Performing Memory Forensics With Volatility3 safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Performing Memory Forensics With Volatility3?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Performing Memory Forensics With Volatility3 free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under Apache-2.

Who published Performing Memory Forensics With Volatility3?

xalgord (@xalgord) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Performing Memory Forensics With Volatility3 by xalgord

Name: Performing Memory Forensics With Volatility3
Author: xalgord

Performing Memory Forensics with Volatility 3

When to Use

When analyzing a RAM dump from a compromised or suspect system
During incident response to identify running malware, injected code, or rootkits
When you need to extract credentials, encryption keys, or network connections from memory
For detecting process hollowing, DLL injection, or hidden processes
When disk-based forensics alone is insufficient and volatile data is critical

Detection Gaps & Validation

Volatility output is only as good as the symbols and the plugins you remember to run. Avoid these failure modes:

Wrong symbols/profile = empty or misleading output. If windows.info/banners can't match a kernel or plugins return zero rows, you have the wrong ISF/symbol pack, not a clean image. Confirm the exact build (e.g., 19041) and fetch matching symbols before concluding "no processes/connections found." A Linux dump needs a banner-matched ISF built from the target kernel's System.map/DWARF.
Acquisition smear and partial dumps. RAM captured from a live, running system is inconsistent across the acquisition window (page smear); pointers may be stale and structures torn. Note acquisition method/time, and treat single-plugin results as candidates to corroborate.
Hidden artifacts need cross-view, not one list. pslist (EPROCESS list) misses unlinked/DKOM-hidden processes — diff against psscan (pool scan). Same for modules vs modscan, and netstat vs netscan. A process in psscan but not pslist, or malfind hits with RWX private memory, are the real leads.
Validate before reporting. Confirm injection by dumping the region (windows.vadinfo/memmap --dump) and checking for PE headers/shellcode, then YARA-scan it; correlate netscan C2 IPs with cmdline, loaded DLLs, and disk/network logs. Extract suspected malware and detonate or hash-match — don't call a malfind hit "Cobalt Strike" on permissions alone.
Interpretation false positives. malfind flags legitimate JIT engines (.NET, browsers, Java) with RWX memory; many svcscan/netscan entries are benign. LISTENING sockets and residual CLOSED/stale connections are not active C2. hashdump/lsadump can return blanks on modern Credential-Guard systems — absence isn't proof creds weren't stolen.

Prerequisites

Python 3.7+ installed
Volatility 3 framework installed (pip install volatility3)
Memory dump in raw, ELF, or crash dump format
Appropriate symbol tables (ISF files) for the target OS version
Sufficient disk space for analysis output (2-3x memory dump size)
Optional: YARA rules for malware scanning in memory

Workflow

Step 1: Acquire Memory Dump and Install Volatility 3

# Install Volatility 3
pip install volatility3

# Or install from source for latest features
git clone https://github.com/volatilityfoundation/volatility3.git
cd volatility3
pip install -e .

# Download Windows symbol tables (ISF packs)
# Place in volatility3/symbols/ directory
wget https://downloads.volatilityfoundation.org/volatility3/symbols/windows.zip
unzip windows.zip -d /opt/volatility3/volatility3/symbols/

# Download Linux and Mac symbol packs
wget https://downloads.volatilityfoundation.org/volatility3/symbols/linux.zip
wget https://downloads.volatilityfoundation.org/volatility3/symbols/mac.zip

# Memory acquisition tools (for live systems):
# Windows: winpmem, DumpIt, FTK Imager
# Linux: LiME (Linux Memory Extractor)
sudo insmod lime-$(uname -r).ko "path=/cases/memory/linux_mem.lime format=lime"

# Verify the memory dump
file /cases/case-2024-001/memory/memory.raw
ls -lh /cases/case-2024-001/memory/memory.raw

Step 2: Identify the Operating System Profile

# Run banners plugin to identify the OS
vol -f /cases/case-2024-001/memory/memory.raw banners

# For Windows, identify the OS version
vol -f /cases/case-2024-001/memory/memory.raw windows.info

# Output example:
# Variable        Value
# Kernel Base     0xf8047e200000
# DTB             0x1ad000
# Symbols         ntkrnlmp.pdb/GUID
# Is64Bit         True
# IsPAE           False
# primary layer   Intel32e
# KdVersionBlock  0xf8047ee232c0
# Major/Minor     15.19041
# Machine Type    34404
# KeNumberProcessors 4
# SystemTime      2024-01-18 14:32:15 UTC
# NtBuildLab      19041.1.amd64fre.vb_release.191206-1406
# NtProductType   NtProductWinNt
# NtSystemRoot    C:\WINDOWS
# PE MajorOperatingSystemVersion 10
# PE MinorOperatingSystemVersion 0

# For Linux memory dumps
vol -f /cases/case-2024-001/memory/linux_mem.lime linux.info

Step 3: Enumerate Processes and Detect Anomalies

# List all running processes
vol -f /cases/case-2024-001/memory/memory.raw windows.pslist | tee /cases/case-2024-001/analysis/pslist.txt

# Show process tree (parent-child relationships)
vol -f /cases/case-2024-001/memory/memory.raw windows.pstree | tee /cases/case-2024-001/analysis/pstree.txt

# Detect hidden processes using cross-view analysis
vol -f /cases/case-2024-001/memory/memory.raw windows.psscan | tee /cases/case-2024-001/analysis/psscan.txt

# Compare pslist vs psscan to find hidden processes
diff <(vol -f memory.raw windows.pslist | awk '{print $1}' | sort) \
     <(vol -f memory.raw windows.psscan | awk '{print $1}' | sort)

# List DLLs loaded by a suspicious process (PID 4532)
vol -f /cases/case-2024-001/memory/memory.raw windows.dlllist --pid 4532

# Check for process hollowing and injection
vol -f /cases/case-2024-001/memory/memory.raw windows.malfind | tee /cases/case-2024-001/analysis/malfind.txt

# Dump suspicious process memory for further analysis
vol -f /cases/case-2024-001/memory/memory.raw windows.memmap --pid 4532 --dump \
   -o /cases/case-2024-001/analysis/dumps/

Step 4: Analyze Network Connections and Registry

# List active network connections
vol -f /cases/case-2024-001/memory/memory.raw windows.netscan | tee /cases/case-2024-001/analysis/netscan.txt

# Filter for established connections
vol -f /cases/case-2024-001/memory/memory.raw windows.netscan | grep ESTABLISHED

# Filter for listening ports
vol -f /cases/case-2024-001/memory/memory.raw windows.netscan | grep LISTENING

# Extract network connections with process mapping
vol -f /cases/case-2024-001/memory/memory.raw windows.netstat | tee /cases/case-2024-001/analysis/netstat.txt

# Dump registry hives from memory
vol -f /cases/case-2024-001/memory/memory.raw windows.registry.hivelist

# Extract specific registry keys
vol -f /cases/case-2024-001/memory/memory.raw windows.registry.printkey \
   --key "Software\Microsoft\Windows\CurrentVersion\Run"

# Check services
vol -f /cases/case-2024-001/memory/memory.raw windows.svcscan | tee /cases/case-2024-001/analysis/services.txt

Step 5: Extract Credentials and Sensitive Data

# Dump cached credentials (hashdump)
vol -f /cases/case-2024-001/memory/memory.raw windows.hashdump | tee /cases/case-2024-001/analysis/hashes.txt

# Extract LSA secrets
vol -f /cases/case-2024-001/memory/memory.raw windows.lsadump

# Dump cached domain credentials
vol -f /cases/case-2024-001/memory/memory.raw windows.cachedump

# Search for plaintext strings in process memory
vol -f /cases/case-2024-001/memory/memory.raw windows.strings --pid 4532 \
   | grep -iE '(password|credential|token|api.key)'

# Extract command history from cmd.exe/powershell
vol -f /cases/case-2024-001/memory/memory.raw windows.cmdline | tee /cases/case-2024-001/analysis/cmdline.txt

# Extract environment variables
vol -f /cases/case-2024-001/memory/memory.raw windows.envars --pid 4532

Step 6: Scan for Malware with YARA Rules

# Scan memory with YARA rules
vol -f /cases/case-2024-001/memory/memory.raw yarascan \
   --yara-file /opt/yara-rules/malware_index.yar | tee /cases/case-2024-001/analysis/yara_hits.txt

# Scan specific process memory
vol -f /cases/case-2024-001/memory/memory.raw yarascan \
   --yara-file /opt/yara-rules/apt_rules.yar --pid 4532

# Check loaded kernel modules for rootkits
vol -f /cases/case-2024-001/memory/memory.raw windows.modules | tee /cases/case-2024-001/analysis/modules.txt

# Detect unlinked/hidden modules
vol -f /cases/case-2024-001/memory/memory.raw windows.modscan | tee /cases/case-2024-001/analysis/modscan.txt

# Check for SSDT hooks (System Service Descriptor Table)
vol -f /cases/case-2024-001/memory/memory.raw windows.ssdt | grep -v "ntoskrnl\|win32k"

# Dump a suspicious executable from memory
vol -f /cases/case-2024-001/memory/memory.raw windows.dumpfiles --pid 4532 \
   -o /cases/case-2024-001/analysis/extracted/

Step 7: Compile Findings into a Report

# Generate comprehensive analysis summary
echo "=== MEMORY FORENSICS REPORT ===" > /cases/case-2024-001/analysis/memory_report.txt
echo "Image: memory.raw" >> /cases/case-2024-001/analysis/memory_report.txt
echo "OS: Windows 10 Build 19041" >> /cases/case-2024-001/analysis/memory_report.txt
echo "" >> /cases/case-2024-001/analysis/memory_report.txt

echo "--- Suspicious Processes ---" >> /cases/case-2024-001/analysis/memory_report.txt
cat /cases/case-2024-001/analysis/malfind.txt >> /cases/case-2024-001/analysis/memory_report.txt

echo "--- Network Connections ---" >> /cases/case-2024-001/analysis/memory_report.txt
cat /cases/case-2024-001/analysis/netscan.txt >> /cases/case-2024-001/analysis/memory_report.txt

echo "--- YARA Matches ---" >> /cases/case-2024-001/analysis/memory_report.txt
cat /cases/case-2024-001/analysis/yara_hits.txt >> /cases/case-2024-001/analysis/memory_report.txt

# Calculate hash of the memory dump for integrity
sha256sum /cases/case-2024-001/memory/memory.raw >> /cases/case-2024-001/analysis/memory_report.txt

Key Concepts

Concept	Description
Volatile data	Information that exists only in RAM and is lost when power is removed
Process hollowing	Technique where malware replaces legitimate process memory with malicious code
DLL injection	Loading unauthorized DLLs into a running process address space
EPROCESS	Windows kernel structure representing a process; basis for process listing
Pool scanning	Searching memory for kernel object signatures to find hidden artifacts
VAD (Virtual Address Descriptor)	Memory management structure tracking process virtual memory regions
ISF (Intermediate Symbol Format)	Volatility 3 symbol table format for OS-specific structure definitions
Malfind	Plugin detecting injected code by examining VAD permissions and content

Tools & Systems

Tool	Purpose
Volatility 3	Primary open-source memory forensics framework
LiME	Linux Memory Extractor for acquiring Linux RAM dumps
WinPmem	Windows physical memory acquisition driver
DumpIt	Comae one-click Windows memory dump utility
YARA	Pattern matching engine for malware signature scanning
Rekall	Alternative memory forensics framework (Google)
MemProcFS	Memory process file system for memory analysis
strings	Extract printable strings from binary memory dumps

Common Scenarios

Scenario 1: Active Malware Investigation Acquire memory with DumpIt, run pslist/pstree to identify suspicious processes, use malfind to detect injected code in svchost.exe, dump the injected memory segment, scan with YARA rules identifying Cobalt Strike beacon, extract C2 IP from netscan, correlate with network logs.

Scenario 2: Credential Theft After Breach Run hashdump and lsadump to extract cached credentials, identify mimikatz execution in cmdline output, check for lsass.exe memory dumps in filesystem artifacts, correlate with lateral movement evidence in network connections.

Scenario 3: Rootkit Detection Compare pslist (uses EPROCESS linked list) with psscan (pool scanning) to find unlinked processes, check modules vs modscan for hidden kernel drivers, examine SSDT for hooks redirecting system calls, dump suspicious modules for static analysis.

Scenario 4: Ransomware Incident Recovery Extract encryption keys from ransomware process memory before system shutdown, identify the ransomware variant using YARA, find the initial execution point through command line artifacts, map lateral movement via network connections.

Output Format

Memory Forensics Analysis:
  Image:            memory.raw (16 GB)
  OS Identified:    Windows 10 x64 Build 19041
  Capture Time:     2024-01-18 14:32:15 UTC

  Process Analysis:
    Total Processes:    87
    Hidden Processes:   2 (PIDs: 4532, 6128)
    Injected Processes: 3 (malfind detections)
    Suspicious:         svchost.exe (PID 4532) - injected code at 0x7FFE0000

  Network Connections:
    Total:        45
    Established:  12
    Suspicious:   3 (C2 connections to 185.xx.xx.xx:443)

  Credentials Found:
    NTLM Hashes:      4 accounts
    Cached Creds:      2 domain accounts

  YARA Matches:
    CobaltStrike_Beacon:  PID 4532 (3 hits)
    Mimikatz_Memory:      PID 6128 (1 hit)

  Extracted Artifacts:   15 files dumped to /analysis/extracted/

Performing Memory Forensics With Volatility3

File contents

Performing Memory Forensics with Volatility 3

When to Use

Detection Gaps & Validation

Prerequisites

Workflow

Step 1: Acquire Memory Dump and Install Volatility 3

Step 2: Identify the Operating System Profile

Step 3: Enumerate Processes and Detect Anomalies

Step 4: Analyze Network Connections and Registry

Step 5: Extract Credentials and Sensitive Data

Step 6: Scan for Malware with YARA Rules

Step 7: Compile Findings into a Report

Key Concepts

Tools & Systems

Common Scenarios

Output Format

Frequently asked questions

Performing Memory Forensics With Volatility3

File contents

Performing Memory Forensics with Volatility 3

When to Use

Detection Gaps & Validation

Prerequisites

Workflow

Step 1: Acquire Memory Dump and Install Volatility 3

Step 2: Identify the Operating System Profile

Step 3: Enumerate Processes and Detect Anomalies

Step 4: Analyze Network Connections and Registry

Step 5: Extract Credentials and Sensitive Data

Step 6: Scan for Malware with YARA Rules

Step 7: Compile Findings into a Report

Key Concepts

Tools & Systems

Common Scenarios

Output Format

Related skills

Frequently asked questions

How do I install the Performing Memory Forensics With Volatility3 skill?

What does the Performing Memory Forensics With Volatility3 skill do?

Is Performing Memory Forensics With Volatility3 safe to use?

Which AI agents work with Performing Memory Forensics With Volatility3?

Is Performing Memory Forensics With Volatility3 free to use?

Who published Performing Memory Forensics With Volatility3?